Estimation of the router causing the silent failure

Abstract

Provided are a router estimation system and a router estimation method that are capable of accurately estimating a router that is the cause of a silent failure in a network slice. A policy manager unit (90) specifies, for each of a plurality of slice communications performed in a communication system, the number of pairs for which the slice communication is included in the pair and for which the degree of correlation reduction associated with the pair satisfies a reduction determination condition. The policy manager unit (90) estimates, as a router that is the cause of a drop in performance of a functional element related to a slice communication, at least one router included in a router group existing on the path of the slice communication, in which the number of identified pairs identified on the basis of router group data satisfies a given number-of-pairs condition.

Description

Technical Field

[0001] This disclosure relates to the estimation of a router that is the cause of a silent failure.

Background Art

[0002] Patent Document 1 describes deploying a plurality of network functions (NFs) included in a network service (NS) to a server in which a container-type application execution environment is installed. Patent Document 1 also describes constructing a network slice and monitoring NFs.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a communication system as described in Patent Document 1, although an abnormality of a router, which is a component of the communication system, has not been detected, a decrease in the performance of a functional element (such as NS or NF) in communication using a network slice available to the functional element included in the communication system (so-called silent failure) may occur. However, it has been difficult to investigate the cause of such a silent failure.

[0005] This disclosure has been made in view of the above circumstances, and one of its objectives is to provide a router estimation system and a router estimation method that can accurately estimate a router that is the cause of a silent failure in a network slice.

Means for Solving the Problems

[0006] The router estimation system according to this disclosure comprises one or more processors, and at least one of the one or more processors performs router group data storage processing, correlation reduction degree calculation processing, reduction determination processing, pair number identification processing, and router estimation processing. In the router group data storage processing, router group data indicating the router group constituting the network slice is stored for each of a plurality of network slices constructed in the communication system. In the correlation reduction degree calculation processing, a correlation reduction degree is calculated, which is the degree of decrease in the strength of the correlation between a pair of slice communications: one slice communication which is a slice communication performed by any functional element included in the communication system using any network slice, and another slice communication which is a slice communication performed by any functional element included in the communication system using any network slice but different from the first slice communication, and a performance index value indicating the performance of the functional element performing the first slice communication in the first slice communication. In the reduction determination processing, for each of the plurality of pairs, it is determined whether the correlation reduction degree associated with the pair satisfies a given reduction determination condition. In the pair count identification process, for each of the multiple slice communications performed by the communication system, the number of pairs that include the slice communication and whose correlation reduction degree associated with the pair satisfies the reduction determination condition is identified. In the router estimation process, at least one router that is included in the router group located on the path of the slice communication, whose number of identified pairs is large enough to satisfy the given pair count condition, is estimated to be the router causing the performance of the functional element related to the slice communication to deteriorate.

[0007] Furthermore, in the router estimation method relating to this disclosure, router group data indicating the group of routers constituting each of a plurality of network slices constructed in the communication system is stored. In addition, a correlation reduction degree is calculated, which is the degree of decrease in the strength of the correlation between a performance index value indicating the performance of the functional element performing the first slice communication and a performance index value indicating the performance of the functional element performing the second slice communication, associated with a pair of slice communications: one slice communication which is a slice communication performed by any functional element included in the communication system using any network slice, and the other slice communication which is a slice communication performed by any functional element included in the communication system using any network slice, but is different from the first slice communication. Furthermore, for each of the plurality of pairs, it is determined whether the correlation reduction degree associated with the pair satisfies a given reduction determination condition. Furthermore, for each of the plurality of slice communications performed in the communication system, the number of pairs that include the slice communication and whose correlation reduction degree satisfies the reduction determination condition is identified. Furthermore, at least one router included in the router group located on the path of the slice communication, whose number of identified pairs, as determined based on the router group data, satisfies a given number of pairs condition, is estimated to be the router causing the performance degradation of the functional element related to the slice communication. [Brief explanation of the drawing]

[0008] [Figure 1] This figure shows an example of a communication system according to one embodiment of the present invention. [Figure 2] This figure shows an example of a communication system according to one embodiment of the present invention. [Figure 3] This diagram schematically illustrates an example of a network service related to one embodiment of the present invention. [Figure 4] This figure shows an example of the relationships between elements constructed in a communication system according to one embodiment of the present invention. [Figure 5]This is a functional block diagram showing an example of a function implemented in a platform system according to one embodiment of the present invention. [Figure 6] This figure shows an example of the data structure of physical inventory data. [Figure 7] This diagram schematically shows an example of the configuration of a group of functional elements that communicate using network slices. [Figure 8] This figure shows an example of segment routing path management data. [Figure 9] This figure shows an example of router group management data. [Figure 10] This figure shows an example of the data structure of correlation data. [Figure 11] This figure shows an example of the data structure for correlation reduction data. [Figure 12A] This figure schematically shows an example of the change in correlation when the degree of correlation decrease does not meet the criteria for determining decrease. [Figure 12B] This figure schematically shows an example of the change in correlation when the degree of correlation decrease satisfies the criteria for determining the decrease. [Figure 12C] This figure schematically shows an example of the change in correlation when the degree of correlation decrease satisfies the criteria for determining the decrease. [Figure 13] This figure shows an example of the data structure for correlation growth data. [Figure 14] This figure schematically shows an example of the change in correlation when the correlation increase rate satisfies the criteria for determining an increase. [Figure 15] This flowchart shows an example of the processing flow performed in a platform system related to a certain model. [Modes for carrying out the invention]

[0009] One embodiment of the present invention will be described in detail below with reference to the drawings.

[0010] FIG. 1 and FIG. 2 are diagrams showing an example of a communication system 1 according to an embodiment of the present invention. FIG. 1 focuses on the locations of the data center groups included in the communication system 1. FIG. 2 focuses on the various computer systems implemented in the data center groups included in the communication system 1.

[0011] As shown in FIG. 1, the data center groups included in the communication system 1 are classified into a central data center 10, a regional data center 12, and an edge data center 14.

[0012] The central data center 10 is, for example, distributed and several are arranged within the area covered by the communication system 1 (for example, within Japan).

[0013] The regional data center 12 is, for example, distributed and dozens are arranged within the area covered by the communication system 1. For example, when the area covered by the communication system 1 is the whole of Japan, one or two regional data centers 12 may be arranged in each prefecture.

[0014] The edge data center 14 is, for example, distributed and thousands are arranged within the area covered by the communication system 1. Also, each of the edge data centers 14 can communicate with communication equipment 18 equipped with an antenna 16. Here, as shown in FIG. 1, one edge data center 14 may be able to communicate with several communication equipment 18. The communication equipment 18 may include a computer such as a server computer. The communication equipment 18 according to the present embodiment performs wireless communication with a UE (User Equipment) 20 via the antenna 16. The communication equipment 18 equipped with the antenna 16 is provided with, for example, an RU (Radio Unit) described later.

[0015] A plurality of servers are arranged in the central data center 10, the regional data center 12, and the edge data center 14 according to the present embodiment, respectively.

[0016] In this embodiment, for example, the central data center 10, the regional data center 12, and the edge data center 14 are able to communicate with each other. Furthermore, the central data centers 10 can communicate with each other, the regional data centers 12 can communicate with each other, and the edge data centers 14 can communicate with each other.

[0017] As shown in Figure 2, the communication system 1 according to this embodiment includes a platform system 30, multiple radio access networks (RANs) 32, multiple core network systems 34, and multiple UEs 20. The core network systems 34, RANs 32, and UEs 20 cooperate with each other to realize a mobile communication network.

[0018] RAN32 is a computer system equipped with an antenna 16, equivalent to an eNB (eNodeB) in a fourth-generation mobile communication system (hereinafter referred to as 4G) or a gNB (NR base station) in a fifth-generation mobile communication system (hereinafter referred to as 5G). In this embodiment, RAN32 is mainly implemented by a group of servers and communication equipment 18 located in an edge data center 14. However, some parts of RAN32 (for example, DU (Distributed Unit), CU (Central Unit), vDU (virtual Distributed Unit), vCU (virtual Central Unit)) may be implemented in a central data center 10, a regional data center 12, or communication equipment 18, rather than in the edge data center 14.

[0019] The core network system 34 is a system equivalent to the EPC (Evolved Packet Core) in 4G or the 5G core (5GC) in 5G. The core network system 34 according to this embodiment is mainly implemented by a group of servers located in the central data center 10 and the regional data center 12.

[0020] The platform system 30 according to this embodiment is configured, for example, on a cloud infrastructure and includes a processor 30a, a storage unit 30b, and a communication unit 30c, as shown in Figure 2. The processor 30a is a program control device such as a microprocessor that operates according to a program installed on the platform system 30. The storage unit 30b is, for example, a memory element such as ROM or RAM, or a solid-state drive (SSD) or hard disk drive (HDD). The storage unit 30b stores programs executed by the processor 30a. The communication unit 30c is, for example, a communication interface such as a NIC (Network Interface Controller) or a wireless LAN (Local Area Network) module. Software-Defined Networking (SDN) may be implemented in the communication unit 30c. The communication unit 30c exchanges data with the RAN 32 and the core network system 34.

[0021] In this embodiment, the platform system 30 is implemented by a group of servers located in the central data center 10. Alternatively, the platform system 30 may be implemented by a group of servers located in the regional data center 12.

[0022] In this embodiment, for example, in response to a purchase request for network services (NS) from a purchaser, the requested network services are built in RAN32 or the core network system 34. The built network services are then provided to the purchaser.

[0023] For example, a purchaser, who is an MVNO (Mobile Virtual Network Operator), is provided with network services such as voice communication services and data communication services. The voice communication services and data communication services provided by this embodiment are ultimately provided to the customer (end user) of the purchaser (MVNO in the above example) who uses the UE20 shown in Figures 1 and 2. This end user can perform voice and data communication with other users via the RAN32 and the core network system 34. Furthermore, the end user's UE20 is able to access data networks such as the Internet via the RAN32 and the core network system 34.

[0024] Furthermore, in this embodiment, IoT (Internet of Things) services may be provided to end users who utilize robotic arms, connected cars, etc. In this case, for example, the end users who utilize robotic arms, connected cars, etc. may become purchasers of the network services according to this embodiment.

[0025] In this embodiment, servers located in the central data center 10, regional data center 12, and edge data center 14 have a container-type virtualization application execution environment such as Docker installed, and containers can be deployed and run on these servers. A cluster consisting of one or more containers generated by such virtualization technology may be constructed on these servers. For example, a Kubernetes cluster managed by a container management tool such as Kubernetes may be constructed. The processors on the constructed cluster may then run container-type applications.

[0026] In this embodiment, the network service provided to the purchaser consists of one or more functional units (e.g., network functions (NFs)). In this embodiment, the functional unit is implemented using an NF realized by virtualization technology. An NF realized by virtualization technology is referred to as a VNF (Virtualized Network Function). The type of virtualization technology used is irrelevant. For example, a CNF (Containerized Network Function) realized by container-type virtualization technology is also included in VNF in this description. In this embodiment, the network service is described as being implemented by one or more CNFs. Furthermore, the functional unit in this embodiment may correspond to a network node.

[0027] Figure 3 is a schematic diagram illustrating an example of a network service in operation. The network service shown in Figure 3 includes several RU40s, several DU42s, several CU44s (CU-CP (Central Unit - Control Plane) 44a and CU-UP (Central Unit - User Plane) 44b), several AMFs (Access and Mobility Management Functions) 46, several SMFs (Session Management Functions) 48, and several UPFs (User Plane Functions) 50 as software elements.

[0028] In the example in Figure 3, RU40, DU42, CU-CP44a, AMF46, and SMF48 correspond to elements of the control plane (C-Plane), while RU40, DU42, CU-UP44b, and UPF50 correspond to elements of the user plane (U-Plane).

[0029] Furthermore, the network service may include other types of network infrastructure (NF) as software elements. Also, the network service is implemented on multiple computer resources (hardware elements) such as servers.

[0030] In this embodiment, for example, a communication service in a certain area is provided by the network service shown in Figure 3.

[0031] In this embodiment, the multiple RU40s, multiple DU42s, multiple CU-UP44bs, and multiple UPF50s shown in Figure 3 belong to a single end-to-end network slice.

[0032] Figure 4 is a schematic diagram illustrating an example of the relationships between elements constructed in the communication system 1 in this embodiment. The symbols M and N shown in Figure 4 represent any integer of 1 or more, indicating the relationship between the number of elements connected by a link. When both ends of a link are a combination of M and N, the elements connected by that link have a many-to-many relationship. When both ends of a link are a combination of 1 and N or 1 and M, the elements connected by that link have a one-to-many relationship.

[0033] As shown in Figure 4, the network service (NS), network function (NF), CNFC (Containerized Network Function Component), pod, and container are arranged in a hierarchical structure.

[0034] NS corresponds to, for example, a network service composed of multiple NFs. Here, NS may also correspond to elements at a granularity such as 5GC, EPC, 5G RAN (gNB), 4G RAN (eNB), etc.

[0035] In 5G, NFs correspond to elements of a granularity such as RU, DU, CU-CP, CU-UP, AMF, SMF, and UPF. In 4G, NFs correspond to elements of a granularity such as MME (Mobility Management Entity), HSS (Home Subscriber Server), S-GW (Serving Gateway), vDU, and vCU. In this embodiment, for example, one NS contains one or more NFs. That is, one or more NFs are under the control of one NS.

[0036] A CNFC corresponds to a granularity element such as DU mgmt or DU Processing. A CNFC may be a microservice deployed on a server as one or more containers. For example, a CNFC may be a microservice that provides some of the functions of DU, CU-CP, CU-UP, etc. Alternatively, a CNFC may be a microservice that provides some of the functions of UPF, AMF, SMF, etc. In this embodiment, for example, one NF contains one or more CNFCs. That is, one or more CNFCs are under one NF.

[0037] A pod refers to the smallest unit for managing Docker containers in Cubanetes, for example. In this embodiment, for example, one CNFC contains one or more pods. That is, one or more pods are under one CNFC.

[0038] In this embodiment, for example, one pod contains one or more containers. That is, one or more containers are under the control of one pod.

[0039] Furthermore, as shown in Figure 4, network slices (NSIs) and network slice subnet instances (NSSIs) are arranged in a hierarchical structure.

[0040] An NSI can be described as an end-to-end virtual circuit spanning multiple domains (for example, from RAN32 to core network system34). An NSI may be a slice for high-speed, high-capacity communication (e.g., for eMBB: enhanced Mobile Broadband), a slice for highly reliable and low-latency communication (e.g., for URLLC: Ultra-Reliable and Low Latency Communications), or a slice for connecting a large number of terminals (e.g., for mMTC: massive Machine Type Communication). An NSSI can also be described as a virtual circuit in a single domain obtained by dividing an NSI. An NSSI may be a slice in the RAN domain, a slice in a transport domain such as the MBH (Mobile Back Haul) domain, or a slice in the core network domain.

[0041] In this embodiment, for example, one NSI contains one or more NSSIs. That is, one or more NSSIs are under the control of one NSI. In this embodiment, multiple NSIs may share the same NSSI.

[0042] Furthermore, as shown in Figure 4, NSSI and NS generally have a many-to-many relationship.

[0043] Furthermore, in this embodiment, for example, one NF can belong to one or more network slices. Specifically, for example, one NF can be configured with NSSAI (Network Slice Selection Assistance Information) that includes one or more S-NSSAI (Sub Network Slice Selection Assist Information). Here, S-NSSAI is information associated with a network slice. Note that an NF does not necessarily have to belong to a network slice.

[0044] Figure 5 is a functional block diagram showing an example of the functions implemented in the platform system 30 according to this embodiment. Note that not all of the functions shown in Figure 5 are required to be implemented in the platform system 30 according to this embodiment, and other functions may also be implemented.

[0045] As shown in Figure 5, the platform system 30 according to this embodiment functionally includes, for example, an Operation Support System (OSS) unit 60, an Orchestration (E2EO: End-to-End-Orchestration) unit 62, a Service Catalog Storage Unit 64, a Big Data Platform Unit 66, a Data Bus Unit 68, an Artificial Intelligence (AI) unit 70, a Monitoring Function Unit 72, an SDN Controller 74, a Configuration Management Unit 76, a Container Management Unit 78, and a Repository Unit 80. The OSS unit 60 includes an Inventory Database 82, a Ticket Management Unit 84, a Fault Management Unit 86, and a Performance Management Unit 88. The E2EO unit 62 includes a Policy Manager Unit 90, a Slice Manager Unit 92, and a Lifecycle Management Unit 94. These elements are mainly implemented as a processor 30a, a storage unit 30b, and a communication unit 30c.

[0046] The functions shown in Figure 5 may be implemented by installing them on a platform system 30, which is one or more computers, and having a processor 30a execute a program containing commands corresponding to those functions. This program may be supplied to the platform system 30 via a computer-readable information storage medium such as an optical disk, magnetic disk, magnetic tape, magneto-optical disk, or flash memory, or via the internet. The functions shown in Figure 5 may also be implemented using circuit blocks, memory, or other LSIs. Furthermore, it will be understood by those skilled in the art that the functions shown in Figure 5 can be realized in various forms, such as hardware only, software only, or a combination thereof.

[0047] The container management unit 78 performs container lifecycle management. For example, processes related to container construction, such as container deployment and configuration, are included in this lifecycle management.

[0048] In this embodiment, the platform system 30 may include a plurality of container management units 78. Each of the plurality of container management units 78 may have a container management tool such as Kubernetes and a package manager such as Helm installed. Each of the plurality of container management units 78 may perform container construction, such as container deployment, on the server group (e.g., a Kubernetes cluster) associated with the container management unit 78.

[0049] The container management unit 78 does not need to be included in the platform system 30. The container management unit 78 may, for example, be located on a server managed by the container management unit 78 (i.e., RAN 32 or the core network system 34), or on another server co-located with the server managed by the container management unit 78.

[0050] In this embodiment, the repository unit 80 stores, for example, container images of containers included in a group of functional units (e.g., an NF group) that implement network services.

[0051] The inventory database 82 is a database that stores inventory information. This inventory information includes, for example, information about servers located in RAN32 and the core network system 34 and managed by the platform system 30.

[0052] In this embodiment, the inventory database 82 stores inventory data. The inventory data shows the current configuration of the elements included in the communication system 1 and the relationships between those elements. The inventory data also shows the status of resources managed by the platform system 30 (for example, resource usage). This inventory data may be physical inventory data or logical inventory data. Physical inventory data and logical inventory data will be described later.

[0053] Figure 6 shows an example of the data structure of physical inventory data. The physical inventory data shown in Figure 6 is associated with a single server. The physical inventory data shown in Figure 6 includes, for example, server ID, location data, building data, floor number data, rack data, specification data, network data, list of active container IDs, cluster ID, etc.

[0054] The server ID included in the physical inventory data is, for example, an identifier for the server associated with that physical inventory data.

[0055] Location data included in physical inventory data is, for example, data indicating the location (e.g., the address of the location) of the server associated with that physical inventory data.

[0056] The building data included in the physical inventory data is, for example, data indicating the building (e.g., building name) where the server associated with that physical inventory data is located.

[0057] The floor number data included in the physical inventory data is, for example, data indicating the floor on which the server associated with that physical inventory data is located.

[0058] The rack data included in the physical inventory data is, for example, an identifier for the rack where the server associated with that physical inventory data is located.

[0059] The specification data included in the physical inventory data is, for example, data that indicates the specifications of the server associated with that physical inventory data, and the specification data includes things like the number of cores, memory capacity, and hard disk capacity.

[0060] The network data included in the physical inventory data is, for example, data that shows information about the network of the server associated with the physical inventory data. The network data includes, for example, the NIC (Network Interface Card) that the server has, the number of ports that the NIC has, and the port IDs of those ports.

[0061] The list of operational container IDs included in the physical inventory data is, for example, data that shows information about one or more containers running on the server associated with the physical inventory data, and the list of operational container IDs shows, for example, a list of instance identifiers (container IDs) of the containers.

[0062] The cluster ID included in the physical inventory data is, for example, the identifier of the cluster (e.g., the Cubanetes cluster) to which the server associated with that physical inventory data belongs.

[0063] The logical inventory data includes topology data showing the current state of relationships between elements, as shown in Figure 4, for multiple elements included in the communication system 1. For example, the logical inventory data includes topology data that includes the identifier of a certain NS and the identifiers of one or more NFs under that NS. Also, for example, the logical inventory data includes topology data that includes the identifier of a certain network slice and the identifiers of one or more NFs belonging to that network slice.

[0064] Furthermore, the inventory data may include data indicating the current status of geographical and topological relationships between elements included in the communication system 1. As mentioned above, the inventory data includes location data indicating the locations where the elements included in the communication system 1 are operating, that is, the current locations of the elements included in the communication system 1. From this, it can be said that the inventory data indicates the current status of geographical relationships between elements (for example, geographical proximity between elements).

[0065] Furthermore, the logical inventory data may include NSI data that indicates information about network slices. NSI data indicates attributes such as the identifier of a network slice instance and the type of network slice. Additionally, the logical inventory data may include NSSI data that indicates information about network slice subnets. NSSI data indicates attributes such as the identifier of a network slice subnet and the type of network slice subnet.

[0066] Furthermore, the logical inventory data may include NS data that indicates information about NS. NS data may, for example, indicate the identifier of an NS instance and attributes such as the type of NS. The logical inventory data may also include NF data that indicates information about NF. NF data may, for example, indicate the identifier of an NF instance and attributes such as the type of NF. The logical inventory data may also include CNFC data that indicates information about CNFC. CNFC data may, for example, indicate attributes such as the identifier of an instance and the type of CNFC. The logical inventory data may also include pod data that indicates information about pods contained within a CNFC. Pod data may, for example, indicate attributes such as the identifier of a pod instance and the type of pod. The logical inventory data may also include container data that indicates information about containers contained within a pod. Container data may, for example, indicate attributes such as the container ID of a container instance and the type of container.

[0067] The container ID in the logical inventory data and the container ID in the list of active container IDs in the physical inventory data are used to associate a container instance with the server on which that instance is running.

[0068] Furthermore, data indicating various attributes such as hostnames and IP addresses may be included in the aforementioned data contained in the logical inventory data. For example, container data may include data indicating the IP address of the container corresponding to that container data. Also, for example, NF data may include data indicating the IP address and hostname of the NF indicated by that NF data.

[0069] Furthermore, the logical inventory data may include data indicating NSSAIs, which include one or more S-NSSAIs, that are set for each NF.

[0070] Furthermore, the inventory database 82 works in conjunction with the container management unit 78 to monitor the status of resources as needed. The inventory database 82 then updates the inventory data stored in it as needed based on the latest status of the resources.

[0071] Furthermore, in response to actions such as the construction of new elements included in communication system 1, the configuration of elements included in communication system 1, scaling of elements included in communication system 1, or replacement of elements included in communication system 1, the inventory database 82 updates the inventory data stored in the inventory database 82.

[0072] The service catalog storage unit 64 stores service catalog data. The service catalog data may include, for example, service template data that shows logic used by the lifecycle management unit 94. This service template data includes information necessary to build network services. For example, the service template data includes information that defines NS, NF, and CNFC, and information that shows the correspondence between NS, NF, and CNFC. Also, for example, the service template data includes a script for a workflow to build network services.

[0073] An example of service template data is an NSD (NS Descriptor). An NSD is associated with a network service and indicates the types of multiple functional units (e.g., multiple CNFs) included in that network service. The NSD may also indicate the number of each type of functional unit, such as a CNF, included in the network service. Furthermore, the NSD may indicate the filename of the CNFD related to the CNFs included in the network service, as described later.

[0074] Another example of service template data is a CNFD (CNF Descriptor). The CNFD may indicate the computer resources required by the CNF (e.g., CPU, memory, hard disk, etc.). For example, the CNFD may indicate the computer resources required by each of the multiple containers included in the CNF (CPU, memory, hard disk, etc.).

[0075] Furthermore, the service catalog data may include information about thresholds (e.g., anomaly detection thresholds) used by the policy manager unit 90 to compare with calculated performance indicator values. Performance indicator values ​​will be described later.

[0076] Furthermore, the service catalog data may also include, for example, slice template data. The slice template data contains information necessary to perform instantiation of network slices, and includes, for example, logic used by the slice manager unit 92.

[0077] Slice template data includes information on the "Generic Network Slice Template" defined by the GSMA (GSM Association) ("GSM" is a registered trademark). Specifically, slice template data includes network slice template data (NST), network slice subnet template data (NSST), and network service template data. Furthermore, slice template data includes information showing the hierarchical structure of these elements, as shown in Figure 4.

[0078] In this embodiment, the lifecycle management unit 94, for example, constructs a new network service in response to a purchase request for an NS from a purchaser.

[0079] The lifecycle management unit 94 may, for example, execute a workflow script associated with the network service to be purchased in response to a purchase request. By executing this workflow script, the lifecycle management unit 94 may instruct the container management unit 78 to deploy the containers included in the newly purchased network service. The container management unit 78 may then retrieve the container image of the container from the repository unit 80 and deploy the container corresponding to the container image to the server.

[0080] Furthermore, in this embodiment, the lifecycle management unit 94 performs scaling and replacement of elements included in the communication system 1, for example. Here, the lifecycle management unit 94 may output container deployment and deletion instructions to the container management unit 78. The container management unit 78 may then perform processing such as container deployment and container deletion in accordance with these instructions. In this embodiment, the lifecycle management unit 94 enables scaling and replacement that cannot be handled by tools such as Kubanetes of the container management unit 78.

[0081] The lifecycle management unit 94 may also output an instruction to the SDN controller 74 to create a communication path. For example, the lifecycle management unit 94 may provide the SDN controller 74 with two IP addresses at both ends of the communication path to be created, and the SDN controller 74 will create a communication path connecting these two IP addresses. The created communication path may be managed in association with these two IP addresses.

[0082] Furthermore, the lifecycle management unit 94 may output an instruction to the SDN controller 74 to create a communication path between the two IP addresses associated with those two IP addresses.

[0083] In this embodiment, the slice manager unit 92 performs, for example, the instantiation of a network slice. In this embodiment, the slice manager unit 92 performs, for example, the instantiation of a network slice by executing the logic indicated by the slice template stored in the service catalog storage unit 64.

[0084] The slice manager unit 92 includes the functions of NSMF (Network Slice Management Function) and NSSMF (Network Slice Sub-network Management Function), as described in, for example, the 3GPP (Third Generation Partnership Project) specification "TS28 533". NSMF is a function that generates and manages network slices and provides management services for NSI. NSSMF is a function that generates and manages network slice subnets that constitute a part of a network slice and provides management services for NSSI.

[0085] Here, the slice manager unit 92 may output configuration management instructions related to the instantiation of network slices to the configuration management unit 76. The configuration management unit 76 may then perform configuration management, such as setting up, in accordance with the said configuration management instructions.

[0086] The slice manager unit 92 may also present two IP addresses to the SDN controller 74 and output an instruction to create a communication path between these two IP addresses.

[0087] In this embodiment, the configuration management unit 76 performs configuration management, such as setting up groups of elements like NFs, in accordance with configuration management instructions received from, for example, the lifecycle management unit 94 or the slice manager unit 92.

[0088] In this embodiment, the SDN controller 74 creates a communication path between two IP addresses associated with a communication path creation instruction, for example, in accordance with the instruction received from the lifecycle management unit 94 or the slice manager unit 92. The SDN controller 74 may create the communication path between the two IP addresses using a known path calculation method, such as Flex Algo.

[0089] For example, the SDN controller 74 may use segment routing technology (e.g., SRv6 (Segment Routing IPv6)) to build NSIs and NSSIs for aggregation routers and servers located between communication paths. Alternatively, the SDN controller 74 may generate NSIs and NSSIs across multiple target NFs by issuing commands to configure a common VLAN (Virtual Local Area Network) for multiple target NFs, and commands to assign the bandwidth and priority indicated in the configuration information to that VLAN.

[0090] Furthermore, the SDN controller 74 may perform actions such as changing the maximum bandwidth available for communication between two IP addresses without constructing a network slice.

[0091] The platform system 30 according to this embodiment may include a plurality of SDN controllers 74. Each of the plurality of SDN controllers 74 may perform processing such as creating communication paths for a group of network devices such as aggregation routers associated with the SDN controller 74.

[0092] Furthermore, in this embodiment, the SDN controller 74 may appropriately modify the created communication path. For example, the SDN controller 74 may detect a failure in a network device associated with the SDN controller 74, and in response to the detection, change the communication path created by the SDN controller 74 that goes through the network device to a communication path that does not go through the network device.

[0093] Furthermore, the lifecycle management unit 94 or the slice manager unit 92 may output a communication path change instruction to the SDN controller 74. The SDN controller 74 may then change the communication path created by the SDN controller 74 in accordance with the change instruction.

[0094] For example, the lifecycle management unit 94 or the slice manager unit 92 may output a communication path modification instruction to the SDN controller 74 that is associated with the identifier of a network device to be excluded from the communication path. The SDN controller 74 may then, upon receiving the modification instruction, modify the communication path created by the SDN controller 74 to a communication path that excludes the network device identified by the identifier associated with the modification instruction (i.e., a communication path that does not pass through the network device identified by the identifier associated with the modification instruction).

[0095] In this embodiment, the monitoring function unit 72 monitors, for example, the group of elements included in the communication system 1 according to a given management policy. Here, the monitoring function unit 72 may monitor the group of elements according to a monitoring policy specified by the purchaser when purchasing the network service, for example.

[0096] In this embodiment, the monitoring function unit 72 performs monitoring at various levels, such as the slice level, NS level, NF level, CNFC level, and hardware level such as the server.

[0097] The monitoring function unit 72 may, for example, configure modules that output metric data to hardware such as a server or software elements included in the communication system 1, so that monitoring can be performed at the various levels described above. For example, an NF may output metric data to the monitoring function unit 72 that indicates metrics that can be measured (identified) in the NF. Alternatively, a server may output metric data to the monitoring function unit 72 that indicates metrics related to hardware that can be measured (identified) in the server.

[0098] Furthermore, for example, the monitoring function unit 72 may deploy a sidecar container on the server that aggregates metric data showing metrics output from multiple containers on a CNFC (microservice) basis. This sidecar container may include an agent called an exporter. The monitoring function unit 72 may repeatedly execute the process of obtaining the aggregated metric data on a microservice basis from the sidecar container at a given monitoring interval, using the mechanism of a monitoring tool such as Prometheus, which can monitor container management tools such as Cubanetes.

[0099] The monitoring function unit 72 may, for example, monitor performance indicator values ​​for performance indicators described in "TS 28.552, Management and orchestration; 5G performance measurements" or "TS 28.554, Management and orchestration; 5G end to end Key Performance Indicators (KPIs)". The monitoring function unit 72 may also acquire metric data indicating the monitored performance indicator values.

[0100] In this embodiment, the monitoring function unit 72 generates performance index value data indicating the performance index values ​​of the elements included in the communication system 1 in a predetermined aggregation unit by performing a process (enrichment) to aggregate metric data in a predetermined aggregation unit.

[0101] For example, performance index data for a gNB is generated by aggregating metric data showing the metrics of elements under that gNB (e.g., network nodes such as DU42 and CU44). In this way, performance index data showing the communication performance in the area covered by the gNB is generated. Here, for example, performance index data showing multiple types of communication performance, such as traffic volume (throughput) and latency, may be generated for each gNB. Alternatively, performance index data showing the communication performance of a certain element (e.g., DU42) during a predetermined period may be generated by aggregating metric data showing the metrics of that element during that predetermined period. Note that the communication performance shown by the performance index data is not limited to traffic volume or latency.

[0102] The monitoring function unit 72 then outputs the performance indicator value data generated by the enrichment described above to the data bus unit 68.

[0103] In this embodiment, the data bus unit 68 receives, for example, performance index value data output from the monitoring function unit 72. The data bus unit 68 then generates a performance index value file containing the received performance index value data (one or more). The data bus unit 68 then outputs the generated performance index value file to the big data platform unit 66.

[0104] Furthermore, elements such as network slices, NS, NF, and CNFC included in the communication system 1, as well as hardware such as servers, notify the monitoring function unit 72 of various alerts (for example, alerts triggered by the occurrence of a failure).

[0105] Then, when the monitoring function unit 72 receives, for example, the notification of the alert mentioned above, it outputs alert message data indicating the notification to the data bus unit 68. The data bus unit 68 then generates an alert file by combining the alert message data indicating one or more notifications into a single file, and outputs the alert file to the big data platform unit 66.

[0106] In this embodiment, the big data platform unit 66 stores, for example, performance indicator value files and alert files output from the data bus unit 68.

[0107] In this embodiment, the AI ​​unit 70 has, for example, several pre-trained machine learning models stored in it. The AI ​​unit 70 uses the various machine learning models stored in it to perform estimation processing, such as future prediction processing of the usage status and service quality of the communication system 1. The AI ​​unit 70 may also generate estimation result data that shows the results of the estimation processing.

[0108] The AI ​​unit 70 may perform estimation processing based on the files stored in the big data platform unit 66 and the machine learning model described above. This estimation processing is suitable when predicting long-term trends at a low frequency.

[0109] Furthermore, the AI ​​unit 70 is capable of acquiring performance indicator data stored in the data bus unit 68. The AI ​​unit 70 may perform estimation processing based on the performance indicator data stored in the data bus unit 68 and the machine learning model described above. This estimation processing is suitable when short-term predictions are made frequently.

[0110] In this embodiment, the performance management unit 88 calculates performance indicator values ​​(e.g., KPIs) based on the metrics indicated by multiple metric data, for example. The performance management unit 88 may also calculate performance indicator values ​​that are an overall evaluation of multiple types of metrics that cannot be calculated from a single metric data (e.g., performance indicator values ​​related to end-to-end network slices). The performance management unit 88 may also generate overall performance indicator value data that shows the performance indicator value which is an overall evaluation.

[0111] The performance management unit 88 may also obtain the performance indicator value file mentioned above from the big data platform unit 66. Furthermore, the performance management unit 88 may obtain estimated result data from the AI ​​unit 70. Based on at least one of the performance indicator value file or the estimated result data, it may calculate performance indicator values ​​such as KPIs. Alternatively, the performance management unit 88 may directly obtain metric data from the monitoring function unit 72. Based on this metric data, it may calculate performance indicator values ​​such as KPIs.

[0112] In this embodiment, the fault management unit 86 detects the occurrence of a fault in the communication system 1 based on, for example, at least one of the metric data, the alert notification, the estimated result data, and the overall performance index value data described above. The fault management unit 86 may, for example, detect the occurrence of a fault that cannot be detected from a single metric data or a single alert notification based on predetermined logic. The fault management unit 86 may generate detected fault data indicating the detected fault.

[0113] Furthermore, the fault management unit 86 may directly obtain metric data and alert notifications from the monitoring function unit 72. The fault management unit 86 may also obtain performance indicator value files and alert files from the big data platform unit 66. Additionally, the fault management unit 86 may obtain alert message data from the data bus unit 68.

[0114] In this embodiment, the policy manager unit 90 performs a predetermined determination process based on at least one of the above-mentioned metric data, performance indicator value data, alert message data, performance indicator value file, alert file, estimated result data, overall performance indicator value data, and detected failure data.

[0115] The policy manager unit 90 may then perform actions according to the result of the determination process. For example, the policy manager unit 90 may output a network slice construction instruction to the slice manager unit 92. Alternatively, the policy manager unit 90 may output a communication path switching instruction to the slice manager unit 92. Furthermore, the policy manager unit 90 may output instructions for scaling or replacing elements to the lifecycle management unit 94 according to the result of the determination process.

[0116] The policy manager unit 90 according to this embodiment is capable of acquiring performance indicator value data stored in the data bus unit 68. The policy manager unit 90 may then perform a predetermined determination process based on the performance indicator value data acquired from the data bus unit 68. Alternatively, the policy manager unit 90 may also perform a predetermined determination process based on alert message data stored in the data bus unit 68.

[0117] In this embodiment, the ticket management unit 84 generates a ticket indicating the content to be notified to the administrator of the communication system 1. The ticket management unit 84 may also generate a ticket indicating the content of the incident data. The ticket management unit 84 may also generate a ticket indicating the values ​​of performance indicator data or metric data. The ticket management unit 84 may also generate a ticket indicating the judgment result by the policy manager unit 90.

[0118] The ticket management unit 84 then notifies the administrator of the communication system 1 of the generated ticket. The ticket management unit 84 may, for example, send an email with the generated ticket attached to the email address of the administrator of the communication system 1.

[0119] In the communication system 1 according to this embodiment, performance degradation of NS, NF, etc. (so-called silent failures) may occur without any abnormalities such as failures being detected.

[0120] The following describes an example of how the platform system 30 according to this embodiment handles the occurrence of silent failures. In the following description, elements with communication functions implemented, such as NS and NF, will be referred to as functional elements.

[0121] The communication system 1 according to this embodiment has multiple network slices constructed within it. Each of the multiple network slices constructed within the communication system 1 according to this embodiment has a separate group of routers configured as a component. However, a common router may be configured as a component across the multiple network slices.

[0122] Figure 7 is a schematic diagram showing an example of the configuration of a group of functional elements that perform communication using one of the multiple network slices constructed in the communication system 1 according to this embodiment.

[0123] The network slice shown in Figure 7 includes multiple segment routing paths 100 as components. Thus, in this embodiment, each of the multiple network slices constructed in the communication system 1 may include one or more segment routing paths 100 as components. In the segment routing paths 100, packet forwarding by segment routing (for example, packet forwarding by SRv6 or SRMPLS (Segment Routing Multi-Protocol Label Switching)) is performed. Furthermore, each of the multiple segment routing paths 100 may include a group of routers as components. Here, a common router may be configured as a component in the multiple segment routing paths 100.

[0124] In this embodiment, each functional element included in the communication system 1 has access to one or more network slices, which are at least a portion of the multiple network slices constructed in the communication system 1. The functional elements included in the communication system 1 are capable of communicating using the network slices available to them. Hereinafter, the communication performed by a functional element using the network slices available to it will be referred to as slice communication.

[0125] In the example in Figure 7, the functional elements that communicate using a network slice include multiple UPF50s (50a, 50b, 50c, ...) and multiple gNB102s (102a, 102b, 102c, ...). Each gNB102 includes a DU42 and a CU44. Note that the functional elements that communicate using the network slice may also include other types of functional elements (e.g., AMF46, SMF48, etc.).

[0126] Furthermore, in the communication system 1 according to this embodiment, the router group constituting each of the multiple network slices constructed in the communication system 1 is managed. Here, for example, the inventory database 82 may store router group data indicating the router group constituting each of the multiple network slices constructed in the communication system 1.

[0127] The router group data according to this embodiment may include, for example, the segment routing path management data illustrated in Figure 8 and the router group management data illustrated in Figure 9.

[0128] The segment routing path management data according to this embodiment is, for example, data indicating one or more segment routing paths 100 through which packets are forwarded in communication performed by a functional element using an available network slice.

[0129] As shown in Figure 8, the segment routing path management data includes, for example, a functional element ID, a slice ID, and a list of segment routing path IDs.

[0130] In segment routing path management data, a functional element ID, which is the identifier of a functional element, is associated with a slice ID, which is the identifier of the network slice on which the functional element can be used. In addition, the segment routing path management data is associated with a segment routing path ID list, which is a list of identifiers (segment routing path IDs) of the segment routing path 100 through which packets are forwarded during communication performed by the functional element using that network slice.

[0131] Here, we assume that the identifiers for gNB102a, gNB102b, and gNB102c are "gNB001", "gNB002", and "gNB003", respectively.

[0132] In this case, the segment routing path management data shown in Figure 8 indicates that gNB102a, gNB102b, and gNB102c all have multiple network slices available, including three network slices with slice IDs "001," "002," and "003," respectively. Note that the available network slices do not need to be common to all functional elements. The available network slices may differ depending on the functional element.

[0133] Hereafter, the network slice with slice ID "001" will be referred to as network slice A. The network slice with slice ID "002" will be referred to as network slice B. The network slice with slice ID "003" will be referred to as network slice C.

[0134] For example, when gNB102a performs slice communication using network slice A, communication is performed using segment routing path 100 whose segment routing path ID is one of "001", "002", "003", ... Similarly, when gNB102a performs slice communication using network slice B, communication is performed using segment routing path 100 whose segment routing path ID is one of "011", "012", "013", ... And when gNB102a performs slice communication using network slice C, communication is performed using segment routing path 100 whose segment routing path ID is one of "021", "022", "023", ...

[0135] Furthermore, when gNB102b performs slice communication using network slice A, communication is performed using segment routing path 100 whose segment routing path ID is one of "101", "102", "103", .... Similarly, when gNB102b performs slice communication using network slice B, communication is performed using segment routing path 100 whose segment routing path ID is one of "111", "112", "113", .... Similarly, when gNB102b performs slice communication using network slice C, communication is performed using segment routing path 100 whose segment routing path ID is one of "121", "122", "123", ....

[0136] Furthermore, when gNB102c performs slice communication using network slice A, communication is performed using segment routing path 100 whose segment routing path ID is one of "201", "202", "203", .... Similarly, when gNB102c performs slice communication using network slice B, communication is performed using segment routing path 100 whose segment routing path ID is one of "211", "212", "213", .... Furthermore, when gNB102c performs slice communication using network slice C, communication is performed using segment routing path 100 whose segment routing path ID is one of "221", "222", "223", ....

[0137] The router group management data according to this embodiment is, for example, data indicating the group of routes that are components of each of the multiple segment routing paths 100.

[0138] As shown in Figure 9, the router group management data includes, for example, a segment routing path ID and a router ID list. The segment routing path ID is the identifier of segment routing path 100. As described above, the segment routing path ID corresponds to an element of the segment routing path ID list included in the segment routing path management data. In the router group management data, the segment routing path ID is associated with the router ID list, which is a list of router identifiers (router IDs) that are components of segment routing path 100 identified by the segment routing path ID.

[0139] For example, the router group management data shown in Figure 9 indicates that the identifiers of multiple routers constituting segment routing path 100 with segment routing path ID "011" are "10000", "10001", "10002", ..., "20001", "20002", ..., respectively. It also indicates that the identifiers of multiple routers constituting segment routing path 100 with segment routing path ID "012" are "10000", "10011", "10012", ..., respectively. Furthermore, it indicates that the identifiers of multiple routers constituting segment routing path 100 with segment routing path ID "013" are "10000", "10021", "10022", ..., respectively.

[0140] Furthermore, it is shown that the identifiers of the multiple routers constituting segment routing path 100 with segment routing path ID "101" are "11000", "11001", "11002", ..., respectively. Furthermore, it is shown that the identifiers of the multiple routers constituting segment routing path 100 with segment routing path ID "102" are "11000", "11011", "11012", ..., "20001", "20002", ..., respectively. Furthermore, it is shown that the identifiers of the multiple routers constituting segment routing path 100 with segment routing path ID "103" are "11000", "11021", "11022", ..., respectively.

[0141] Furthermore, it is shown that the identifiers of the multiple routers constituting segment routing path 100 with segment routing path ID "221" are "12000", "12001", "12002", ..., respectively. Furthermore, it is shown that the identifiers of the multiple routers constituting segment routing path 100 with segment routing path ID "222" are "12000", "12011", "12012", ..., respectively. Furthermore, it is shown that the identifiers of the multiple routers constituting segment routing path 100 with segment routing path ID "223" are "12000", "12021", "12022", ..., "20001", "20003", ..., respectively.

[0142] In the example in Figure 9, the router with router ID "20001" is a common component of three segment routing paths 100 with segment routing path IDs "011", "102", and "223", respectively. Additionally, the router with router ID "20002" is a common component of two segment routing paths 100 with segment routing path IDs "011" and "102", respectively.

[0143] Furthermore, in this embodiment, as described above, the slice manager unit 92, the lifecycle management unit 94, or the SDN controller 74 may change the segment routing path 100, which is a component of the network slice, or the router, which is a component of the segment routing path 100.

[0144] In this embodiment, when such a change in components occurs, the router group data stored in the inventory database 82 (for example, the segment routing path management data shown in Figure 8 and the router group management data shown in Figure 9) is updated.

[0145] Therefore, by referring to the router group data, it is possible to identify the segment routing path 100, which is the current component of the network slice, and the router group, which is the current component of the segment routing path 100.

[0146] In this embodiment, for example, the monitoring function unit 72 monitors the performance of each of the multiple functional elements included in the communication system 1 in slice communication using the network slice for each network slice in which the functional element is available.

[0147] Specifically, for example, the performance of gNB102a in slice communication using network slice A, the performance of gNB102a in slice communication using network slice B, the performance of gNB102a in slice communication using network slice C, the performance of gNB102b in slice communication using network slice A, the performance of gNB102b in slice communication using network slice B, the performance of gNB102b in slice communication using network slice C, the performance of gNB102c in slice communication using network slice A, the performance of gNB102c in slice communication using network slice B, and the performance of gNB102c in slice communication using network slice C are all monitored.

[0148] The monitoring function unit 72 then generates performance index value data for each network slice in which a functional element is available, for example, at predetermined time intervals (e.g., 15-minute intervals), indicating the performance of the functional element in slice communication using that network slice over the most recent predetermined period (e.g., the most recent 15 minutes). The monitoring function unit 72 then outputs the generated performance index value data to the data bus unit 68 at the time interval.

[0149] For example, when performance index data indicating performance over a certain period is generated, performance index data associated with that period may be output to the data bus unit 68. For example, performance index data associated with period data indicating the start and end dates of that period may be output to the data bus unit 68.

[0150] The policy manager unit 90 may also acquire the output performance indicator data in response to the output of performance indicator data to the data bus unit 68.

[0151] Examples of performance metrics represented by performance indicator data include throughput, number of bearer connections, number of attachments, and communication speed (bandwidth). Alternatively, a comprehensive value calculated based on multiple performance indicators (e.g., throughput and number of bearer connections) (e.g., a linear combination of multiple performance indicators) may be used as the performance indicator data value. However, the performance represented by performance indicator data is not limited to those described above.

[0152] In this embodiment, for example, the policy manager unit 90 selects a pair of slice communications: one slice communication performed by a functional element included in the communication system 1 using one of the network slices, and the other slice communication performed by a functional element included in the communication system 1 using one of the network slices, but different from the first slice communication. In other words, two different slice communications are selected to form a pair.

[0153] The network slice on which one slice communication takes place may be the same network slice on which the other slice communication takes place. For example, one slice communication may be a slice communication performed by gNB102a using network slice A, and the other slice communication may be a slice communication performed by gNB102b using network slice A.

[0154] Alternatively, the network slice on which one slice communication takes place may be a different network slice from the network slice on which the other slice communication takes place. For example, one slice communication may be a slice communication performed by gNB102a using network slice B, and the other slice communication may be a slice communication performed by gNB102b using network slice A.

[0155] The policy manager unit 90 then acquires, for example, multiple performance index value data sets, each showing a performance index value indicating the performance of the functional element performing the slice communication in the one slice communication during the most recent predetermined time period, and multiple performance index value data sets, each showing a performance index value indicating the performance of the functional element performing the other slice communication in the other slice communication.

[0156] Hereinafter, the performance index data set containing multiple performance index data representing the performance of each functional element performing the slice communication in one slice communication will be referred to as the first performance index data set. Similarly, the performance index data set containing multiple performance index data representing the performance of each functional element performing the other slice communication in the other slice communication will be referred to as the second performance index data set.

[0157] For example, if the predetermined time length is 3 hours and performance index data is acquired at 15-minute intervals, then the first performance index data set and the second performance index data set will each contain 12 performance index data points.

[0158] Here, the period over which each of the multiple performance indicator data points included in the first performance indicator data set is associated is the same as the period over which each of the multiple performance indicator data points included in the second performance indicator data set is associated.

[0159] The policy manager unit 90 then calculates a correlation coefficient (e.g., correlation coefficient) that indicates the strength of the correlation between the performance indicator values ​​shown by multiple performance indicator data included in the first performance indicator data group and the performance indicator values ​​shown by multiple performance indicator data included in the second performance indicator data group.

[0160] Furthermore, the length of the period from the start of the earliest associated period among the multiple performance indicator data included in the first performance indicator data set to the end of the latest associated period corresponds to the predetermined time length mentioned above. Similarly, the length of the period from the start of the earliest associated period among the multiple performance indicator data included in the second performance indicator data set also corresponds to the predetermined time length mentioned above. The calculated correlation is then associated with the said predetermined time length.

[0161] Then, the policy manager unit 90 generates correlation data based on the calculated correlation, the data structure of which is shown in Figure 10 as an example.

[0162] As shown in Figure 10, the correlation data includes, for example, the first slice communication ID, the second slice communication ID, and date and time data.

[0163] The correlation data is set to a correlation value calculated, for example, as described above.

[0164] The first slice communication ID is the identifier for that slice communication. The first slice communication ID includes, for example, a combination of the first functional element ID, which is the identifier of the functional element performing that slice communication, and the first slice ID, which is the identifier of the network slice on which that slice communication takes place.

[0165] The second slice communication ID is the identifier for the other slice communication. The second slice communication ID includes, for example, a combination of the second functional element ID, which is the identifier of the functional element performing the other slice communication, and the second slice ID, which is the identifier of the network slice on which the other slice communication takes place.

[0166] The date and time data is, for example, data indicating a date and time that represents the period associated with the correlation shown in the correlation data. Here, for example, the date and time data may indicate the date and time that is the end or start of the period associated with the correlation. Alternatively, the date and time data may indicate the date and time that is the start date and time and the date and time that is the end date and time associated with the correlation.

[0167] In this embodiment, for example, correlation data is generated for each pair of slice communications performed by any of the multiple functional elements included in the communication system 1 using any of the network slices. Hereinafter, a pair of slice communications performed by any of the multiple functional elements included in the communication system 1 using any of the network slices will also be referred to as a slice communication pair.

[0168] The timing of the generation of correlation data is not particularly limited. For example, correlation data may be generated each time performance metric data is acquired, based on the most recent multiple performance metric data. In this case, the periods associated with the correlations shown by the sequentially generated correlation data will partially overlap.

[0169] Furthermore, correlation data may be generated based on the latest multiple performance metric data at time intervals corresponding to the length of the period associated with the correlation. For example, in the above example, correlation data may be generated every 3 hours. In this case, the periods associated with the correlations shown by the sequentially generated correlation data will not overlap.

[0170] The policy manager unit 90 then calculates a correlation reduction degree, which is the degree of decrease in the strength of the correlation between a pair of slice communications: one slice communication, which is a slice communication performed by any functional element in the communication system 1 using any network slice, and another slice communication, which is a slice communication performed by any functional element in the communication system 1 using any network slice but is different from the first slice communication. For example, the correlation reduction degree is calculated based on multiple correlation data where the associated first slice communication ID and second slice communication ID are the same and the generated order is consecutive. Here, the correlation reduction degree may also be the degree of decrease in the correlation coefficient of the performance indicator values.

[0171] Here, the policy manager unit 90 may calculate a correlation reduction value corresponding to the combination of the first slice communication ID and the second slice communication ID by subtracting the value of the correlation data for which the date and time indicated by the associated date and time data is the most recent from the value of the correlation data for which the date and time indicated by the associated date and time data is the second most recent from among the multiple correlation data associated with a specific first slice communication ID and a specific second slice communication ID.

[0172] Alternatively, the policy manager unit 90 may extract a predetermined number of correlation data from among the multiple correlation data associated with the first slice communication ID and the second slice communication ID, in order from the most recent date and time indicated by the date and time data. The policy manager unit 90 may then calculate the mean and standard deviation of the extracted correlation data values. Hereinafter, the value obtained by subtracting twice the calculated standard deviation from the calculated mean will be expressed as v1. That is, if the calculated mean is m and the calculated standard deviation is s, then the value v1 corresponds to the value (m-2s).

[0173] The policy manager unit 90 may also calculate a value obtained by subtracting the value of the correlation data for which the date and time indicated by the associated date and time data is the most recent from the value v1, as the correlation reduction value corresponding to the combination of the first slice communication ID and the second slice communication ID.

[0174] Furthermore, the policy manager unit 90 may extract from a predetermined number of correlation data, starting with the most recent date and time indicated by the associated date and time data, the correlation data whose value is smaller than the value v1 described above. The policy manager unit 90 may then calculate the sum of the values ​​obtained by subtracting the value of the extracted correlation data from the value v1 as the correlation reduction value corresponding to the combination of the first slice communication ID and the second slice communication ID.

[0175] Note that examples of correlation reduction are not limited to those described above.

[0176] The policy manager unit 90 may then generate correlation reduction data, an example of which is shown in Figure 11, based on the correlation reduction rate calculated as described above.

[0177] As shown in Figure 11, the correlation reduction data is associated with, for example, the first slice communication ID, the second slice communication ID, and date and time data.

[0178] The correlation reduction data is set to a correlation reduction value calculated as described above, for example.

[0179] The first slice communication ID and the second slice communication ID are set to, for example, the first slice communication ID corresponding to the degree of correlation reduction and a specific second slice communication ID, respectively.

[0180] The date and time data includes, for example, a date and time representative of the multiple correlation data when the degree of correlation reduction is calculated based on multiple correlation data. Here, for example, the date and time indicated by the date and time data for the most recent associated date and time among the multiple correlation data may be set as the value of the date and time data for the degree of correlation reduction. Alternatively, the date and time indicated by the date and time data for the oldest associated date and time among the multiple correlation data may be set as the value of the date and time data for the degree of correlation reduction.

[0181] In this embodiment, for example, correlation reduction data is generated for each slice communication pair.

[0182] Then, in this embodiment, the policy manager unit 90 obtains a correlation reduction degree, which is the degree of decrease in the strength of the correlation between a pair of slice communications: one slice communication which is a slice communication performed by any functional element included in the communication system 1 using any network slice, and another slice communication which is a slice communication performed by any functional element included in the communication system 1 using any network slice but is different from the first slice communication. The policy manager unit 90 then obtains a correlation reduction degree, which is the degree of decrease in the strength of the correlation between a performance index value indicating the performance of the functional element performing the first slice communication and a performance index value indicating the performance of the functional element performing the other slice communication.

[0183] Then, in this embodiment, the policy manager unit 90 determines, for example, whether the correlation reduction degree associated with each of the multiple slice communication pairs satisfies a given reduction determination condition.

[0184] Here, the policy manager unit 90 may acquire the correlation reduction data described above. The policy manager unit 90 may then determine, based on the acquired correlation reduction data, whether the value of the correlation reduction data satisfies the reduction judgment condition.

[0185] Here, the policy manager unit 90 may acquire the latest multiple correlation reduction degree data. The policy manager unit 90 may then determine whether the combination of the multiple correlation reduction degree data satisfies the reduction determination condition.

[0186] For example, suppose the correlation reduction value is the value obtained by subtracting the correlation value of the most recent correlation data from the correlation value of the second most recent correlation data. In this case, the reduction judgment condition may be "the correlation reduction value is greater than or equal to a predetermined value." Alternatively, the reduction judgment condition may be "the correlation reduction values ​​of a predetermined number (e.g., 3) of the most recent correlation data are all greater than or equal to a predetermined value."

[0187] Furthermore, for example, suppose the correlation reduction value is the value obtained by subtracting the correlation data value for the most recent date and time indicated by the associated date and time data from the value v1 mentioned above. In this case, the reduction judgment condition may be "the correlation reduction data value is positive." Alternatively, the reduction judgment condition may be "a predetermined number (e.g., 3) of correlation reduction data values ​​for the most recent date and time indicated by the associated date and time data are all positive."

[0188] Furthermore, for example, the correlation reduction value may be the sum of the values ​​obtained by subtracting the value of at least one extracted correlation data from the value v1. In this case, the reduction determination condition may be "the correlation reduction data value is greater than or equal to a predetermined number."

[0189] Figure 12A schematically shows an example of the change in correlation when the degree of correlation decrease does not meet the criteria for a decrease. Figures 12B and 12C schematically show an example of the change in correlation when the degree of correlation decrease meets the criteria for a decrease.

[0190] In Figures 12A, 12B, and 12C, the horizontal axis represents the date and time t that best represents the period associated with the correlation, and the vertical axis represents the correlation r.

[0191] For example, as shown in Figure 12B, a case where the latest correlation is significantly lower than the previous correlation is a typical example of when the correlation decrease satisfies the criteria for a decrease.

[0192] Furthermore, as shown in Figure 12C, a case where the correlation degree fluctuates significantly is a typical example of a situation where the correlation decrease rate satisfies the criteria for a decrease.

[0193] In this embodiment, for example, the policy manager unit 90 identifies, for each of the multiple slice communications performed in the communication system 1, the number of slice communication pairs that include the slice communication in question and whose correlation reduction degree associated with the slice communication pair satisfies the reduction determination condition (hereinafter referred to as the number of relevant pairs).

[0194] In this embodiment, for example, the policy manager unit 90 determines whether the number of pairs identified for each of the multiple slice communications performed in the communication system 1 satisfies a given pair number condition.

[0195] In this embodiment, for example, the policy manager unit 90 identifies a group of routers located on a slice communication path where the number of identified slice communication pairs satisfies a given pair count condition, based on the router group data. The policy manager unit 90 then estimates that at least one router included in the identified group of routers is the router causing the performance of the functional elements related to the slice communication to be degraded. Here, as described above, the path may be a path where packets are forwarded by segment routing.

[0196] For example, the pair count condition may be "the number of relevant pairs is greater than a predetermined number." In this case, the policy manager unit 90 estimates that at least one router included in the group of routers located on the path of a slice communication where the number of identified slice communication pairs is greater than a predetermined number is the router causing the performance degradation of the functional elements related to the slice communication.

[0197] Furthermore, the pair count condition may be "the ratio of the number of slice communication pairs containing the slice communication, where the correlation reduction degree associated with the slice communication pair satisfies the reduction judgment condition, to the total number of slice communication pairs containing the slice communication, and this ratio is greater than a predetermined ratio." In this case, the policy manager unit 90 may identify, for each of the multiple slice communications performed in the communication system 1, the ratio of the number of slice communication pairs containing the slice communication, where the correlation reduction degree associated with the slice communication pair satisfies the reduction judgment condition, to the total number of slice communication pairs containing the slice communication. The policy manager unit 90 may then estimate at least one router included in the group of routers located on the path of a slice communication where the identified ratio is greater than a predetermined ratio as the router causing the performance of the functional element related to the slice communication to be degraded.

[0198] For example, suppose the slice communication performed by gNB102a using network slice B satisfies the pair count condition. In this case, for example, in the segment routing path management data, the segment routing path IDs included in the segment routing path ID list associated with functional element ID "gNB001" and slice ID "002" are identified. The segment routing path IDs identified here are, for example, "011", "012", "013", etc.

[0199] Then, for each of the segment routing path IDs identified in this way, the router IDs included in the router ID list associated with that segment routing path ID are identified in the router group management data.

[0200] Here, for example, in the router group management data, the router ID list associated with segment routing path ID "011", the router ID list associated with segment routing path ID "012", and the router ID list associated with segment routing path ID "013" are identified.

[0201] Then, a router ID is identified that is included in at least one of the router ID lists identified in this way. Hereafter, the group of router IDs that includes the router IDs identified in this way will be called the group of candidate router IDs that are the cause of the problem. For example, "10000", "10001", "10002", "10011", "10012", "10021", "10022", "20001", "20002", ... are identified as the group of candidate router IDs that are the cause of the problem and are associated with gNB102a and network slice B.

[0202] In this embodiment, the router identified by the router ID included in the group of candidate router IDs identified in this way is presumed to be the cause of the degraded performance of gNB102a. Alternatively, all routers identified by the router ID included in the group of candidate router IDs may be presumed to be the router causing the degraded performance of gNB102a.

[0203] Furthermore, in this embodiment, there may be multiple slice communications that satisfy the pair count condition due to the large number of identified slice communication pairs. In this case, the policy manager unit 90 may estimate that at least one router included in any of the router groups present on each of the paths of the multiple slice communications is the router causing the performance of the multiple functional elements related to the multiple slice communications to be degraded.

[0204] For example, let's assume that the number of pairs condition is also met for the slice communication performed by gNB102a using network slice B, the slice communication performed by gNB102b using network slice A, and the slice communication performed by gNB102c using network slice C.

[0205] In this case, as described above, "10000", "10001", "10002", "10011", "10012", "10021", "10022", "20001", "20002", ... will be identified as the group of candidate router IDs associated with gNB102a and network slice B.

[0206] Similarly, "11000", "11001", "11002", "11011", "11012", "11021", "11022", "20001", "20002", ... are identified as a group of potential causal router IDs that correspond to gNB102b and network slice A.

[0207] Then, "12000", "12001", "12002", "12011", "12012", "12021", "12022", "20001", "20003", ... are identified as the group of candidate router IDs that can be associated with gNB102c and network slice C.

[0208] The router ID "20001," which is included in all three of these candidate router ID groups, is presumed to be the router ID of the router causing the performance degradation of the functional element.

[0209] Furthermore, there is no particular limit to the number of routers that are estimated to be the cause of the performance degradation of the functional elements.

[0210] In this embodiment, for example, the slice manager unit 92 may output an instruction to the SDN controller 74 to change the communication path for each of the one or more routers that are presumed to be the cause of the performance degradation of the functional elements, for the network slice that includes the router as a component. The SDN controller 74 may then change the communication path created by the SDN controller 74 in accordance with the change instruction.

[0211] For example, the slice manager unit 92 may output an instruction to change the communication path associated with the router ID of a router that is presumed to be the cause of the performance degradation of a functional element to the SDN controller 74. Then, in response to receiving the instruction, the SDN controller 74 may change the communication path it created to a communication path that excludes the router identified by the router ID (i.e., a communication path that does not go through that router).

[0212] Furthermore, the administrator of the platform system 30 may check whether each router suspected of being the cause of the performance degradation of the functional elements is experiencing any abnormalities such as failures or capacity overloads. The administrator of the platform system 30 may then output an instruction to the SDN controller 74 to remove any routers in which abnormalities have been confirmed from the communication path. In response to receiving this instruction, the SDN controller 74 may change the communication path it created to exclude the router (i.e., a communication path that does not pass through the router).

[0213] Furthermore, the SDN controller 74 or the slice manager unit 92 may update the segment routing path management data shown in Figure 8 or the router group management data shown in Figure 9, which are stored in the inventory database 82, in accordance with changes in the communication path.

[0214] Even if no abnormality is detected in the router, which is a component of the communication system 1 according to this embodiment, a performance degradation (so-called silent failure) of the functional element included in the communication system 1 may occur in communication using an available network slice.

[0215] Here, if the performance of a certain functional element deteriorates, it is likely that the correlation between the performance indicator values ​​of the functional elements in a pair of functional elements that includes that functional element will decrease.

[0216] Based on this, in this embodiment, as described above, for each of the multiple slice communications performed by the communication system 1, the number of slice communication pairs that include the slice communication in question and whose correlation reduction degree associated with the slice communication pair satisfies the reduction determination condition is identified. Then, the number of identified slice communication pairs is such that at least one router included in the group of routers located on the path of the slice communication that satisfies the given number of pairs condition is estimated to be the router causing the performance of the functional element related to the slice communication to deteriorate.

[0217] In this way, according to this embodiment, the router causing the silent failure in the network slice can be accurately estimated.

[0218] Furthermore, in this embodiment, the policy manager unit 90 may classify the multiple slice communications performed by the communication system 1 into multiple slice communication groups based on the strength of the correlation between performance indicator values ​​identified for each slice communication pair.

[0219] For example, using a general clustering technique, multiple slice communications performed in communication system 1 may be classified into multiple slice communication groups. Here, for example, in each slice communication group, the classification may be performed such that the latest correlation value for any slice communication pair in which the slice communications included in that group are components is greater than a predetermined value.

[0220] The policy manager unit 90 may also calculate a correlation reduction rate for each of the multiple slice communication groups, which is the degree of decrease in the strength of the correlation between a performance index value indicating the performance of the functional element that performs the first slice communication in the first slice communication and a performance index value indicating the performance of the functional element that performs the second slice communication in the second slice communication, associated with a pair of slice communications: one slice communication that is included in the slice communication group and the other slice communication that is included in the slice communication group but is different from the first slice communication.

[0221] Furthermore, the policy manager unit 90 may determine, for each of the multiple slice communication groups, whether the correlation reduction degree associated with each pair of slice communications included in the slice communication group satisfies the reduction determination condition corresponding to the slice communication group.

[0222] The policy manager unit 90 may then determine a threshold for each of the multiple slice communication groups based on the total number of slice communications included in that slice communication group. For example, a value equivalent to 50% of the total number of slice communications, or a value equivalent to 70% of the total number of slice communications, may be determined as the threshold.

[0223] Furthermore, the policy manager unit 90 may estimate that at least one router included in the group of routers located on the path of a slice communication where the value representing the number of identified slice communication pairs is greater than the threshold corresponding to the slice communication group including the slice communication pair is the router causing the performance degradation of the functional elements related to the slice communication.

[0224] Alternatively, the policy manager unit 90 may determine, for each slice communication included in the slice communication group, the ratio of the number of pairs containing the slice communication that satisfy the correlation reduction criteria, relative to the total number of pairs containing the slice communication.

[0225] Furthermore, the policy manager unit 90 may estimate that at least one router included in the group of routers located on the path of a slice communication where the identified proportion is greater than a predetermined proportion is the router causing the performance degradation of the functional elements related to the slice communication.

[0226] In this way, for each slice communication group, the router causing the performance degradation of a functional element is estimated based on a relatively small number of slice communications. This reduces the computational load required to estimate the router causing the performance degradation of the functional element.

[0227] Furthermore, in this embodiment, the policy manager unit 90 may calculate a correlation increase degree, which is the degree of increase in the strength of the correlation between a performance index value indicating the performance of the first functional element in the first slice communication and a performance index value indicating the performance of the second functional element in the second slice communication, associated with a pair of slice communications performed by any of the multiple functional elements included in the communication system 1 using any of the network slices, namely, a first slice communication performed by the first functional element using the first network slice and a second slice communication performed by the second functional element using the second network slice. For example, the correlation increase degree, which is the degree of increase in the strength of the correlation of the performance index values, may be calculated based on a plurality of correlation data where the associated first slice communication ID and second slice communication ID are the same and the generated order is consecutive. Here, the correlation increase degree may also be the degree of increase in the correlation coefficient of the performance index values.

[0228] Here, the policy manager unit 90 may calculate a correlation increase value corresponding to the combination of the first slice communication ID and the second slice communication ID by subtracting the value of the correlation data for which the date and time indicated by the associated date and time data is second to last from the value of the correlation data for which the date and time indicated by the associated date and time data is most recent, from among the multiple correlation data associated with a specific first slice communication ID and a specific second slice communication ID.

[0229] Alternatively, the policy manager unit 90 may extract a predetermined number of correlation data from among multiple correlation data associated with a specific first slice communication ID and a specific second slice communication ID, in order from the most recent date and time indicated by the date and time data. The policy manager unit 90 may then calculate the mean and standard deviation of the extracted correlation data values. Hereinafter, the value obtained by adding twice the calculated standard deviation to the calculated mean will be expressed as v2. That is, if the calculated mean is m and the calculated standard deviation is s, then the value v2 corresponds to the value (m+2s).

[0230] Furthermore, the policy manager unit 90 may calculate the correlation increase value corresponding to the combination of the first slice communication ID and the second slice communication ID by subtracting the value v2 from the value of the correlation data for which the date and time indicated by the associated date and time data is the most recent among the multiple correlation data associated with the first slice communication ID and the second slice communication ID.

[0231] Note that examples of correlation increase are not limited to those described above.

[0232] The policy manager unit 90 may then generate correlation increase data, an example of which is shown in Figure 13, based on the correlation increase calculated as described above.

[0233] As shown in Figure 13, the correlation increase data may be associated with, for example, the first slice communication ID, the second slice communication ID, and date and time data.

[0234] The correlation growth data is set to the correlation growth value calculated as described above, for example.

[0235] The first slice communication ID and the second slice communication ID are set to, for example, the first slice communication ID and the second slice communication ID corresponding to the correlation increase, respectively.

[0236] The date and time data includes, for example, a date and time representative of the multiple correlation data when the correlation increase is calculated based on multiple correlation data. Here, for example, the date and time indicated by the date and time data for the most recent associated date and time among the multiple correlation data may be set as the value of the date and time data for the correlation increase data. Alternatively, the date and time indicated by the date and time data for the oldest associated date and time among the multiple correlation data may be set as the value of the date and time data for the correlation increase data.

[0237] In this embodiment, for example, correlation increase data is generated for each slice communication pair.

[0238] The policy manager unit 90 may also obtain a correlation increase, which is the degree of increase in the strength of the correlation between a performance index value indicating the performance of the functional element performing the first slice communication in the first slice communication and a performance index value indicating the performance of the functional element performing the other slice communication in the other slice communication, associated with a pair of slice communications: one slice communication in which a functional element included in the communication system 1 performs slice communication using any network slice, and the other slice communication in which a functional element included in the communication system 1 performs slice communication using any network slice but different from the first slice communication.

[0239] The policy manager unit 90 may then determine, for each of the multiple slice communication pairs, whether the correlation increase associated with that pair satisfies a given increase determination condition.

[0240] The policy manager unit 90 may then acquire the correlation increase data mentioned above. The policy manager unit 90 may then determine, based on the acquired correlation increase data, whether the value of the correlation increase data satisfies the increase determination condition.

[0241] The policy manager unit 90 may also acquire the latest multiple correlation increase data. The policy manager unit 90 may then determine whether the combination of values ​​in the multiple correlation increase data satisfies the increase determination condition.

[0242] For example, suppose the correlation growth rate is the value obtained by subtracting the correlation growth rate value for the second most recent date and time represented by the associated date and time data from the correlation growth rate value for the most recent date and time represented by the associated date and time data. In this case, the condition for determining increase may be "the correlation growth rate value is greater than or equal to a predetermined value." Alternatively, the condition for determining increase may be "the correlation growth rate values ​​for a predetermined number (e.g., 3) of associated date and time data, starting from the most recent date and time, are all greater than or equal to a predetermined value."

[0243] Furthermore, for example, suppose the correlation growth rate is the value obtained by subtracting the above-mentioned value v2 from the correlation data value for the most recent date and time indicated by the associated date and time data. In this case, the condition for determining increase may be "the value of the correlation growth rate data is positive." Alternatively, the condition for determining increase may be "the values ​​of a predetermined number (e.g., 3) of correlation growth rate data, starting from the most recent date and time indicated by the associated date and time data, are all positive."

[0244] Figure 14 schematically shows an example of the change in correlation when the correlation increase satisfies the criteria for increasing correlation. In Figure 14, the horizontal axis represents the date and time t that represents the period associated with the correlation, and the vertical axis represents the correlation r.

[0245] For example, as shown in Figure 14, a case where the latest correlation index has increased significantly from the previous one is a typical example of a correlation index increase that satisfies the criteria for an increase.

[0246] Furthermore, if the correlation increase associated with a slice communication pair satisfies the increase determination condition, the policy manager unit 90 may estimate that at least one router, which is included in either the first router group located on the path of one slice communication included in the slice communication pair and the second router group located on the path of the other slice communication included in the slice communication pair, is the router causing the performance of the functional elements related to the slice communication pair to deteriorate, based on the router group data.

[0247] For example, suppose the correlation increase associated with a pair of slice communications, one slice communication and the other slice communication, is determined to satisfy a given increase determination condition. Then, the policy manager unit 90 may, for example, identify a first group of routers located on the path of the one slice communication. The policy manager unit 90 may then, for example, identify a second group of routers located on the path of the other slice communication. Here, as described above, the path may be a path where packets are forwarded by segment routing.

[0248] The policy manager unit 90 may then estimate that at least one router included in either the first group of routers or the second group of routers identified in this manner is the router causing the performance degradation of the functional element.

[0249] For example, suppose that the correlation growth data, in which the associated first functional element ID, first slice ID, second functional element ID, and second slice ID are gNB001, 002, gNB002, and 001 respectively, determines that the correlation growth value indicated by the correlation growth data satisfies the growth determination condition.

[0250] In this case, as described above, "10000", "10001", "10002", "10011", "10012", "10021", "10022", "20001", "20002", ... will be identified as the group of candidate router IDs associated with gNB102a and network slice B.

[0251] Furthermore, as mentioned above, "11000", "11001", "11002", "11011", "11012", "11021", "11022", "20001", "20002", ... are identified as the group of potential causal router IDs that correspond to gNB102b and network slice A.

[0252] Furthermore, the router IDs "20001" and "20002," which are included in both of these two candidate router ID groups, may be estimated as the router IDs of the router causing the performance degradation of the functional element.

[0253] In this way, by using not only the correlation decrease but also the correlation increase to estimate the router causing the performance degradation of a functional element, it becomes possible to more accurately identify the router causing the silent failure.

[0254] Furthermore, in this embodiment, the policy manager unit 90 may exclude at least one router that is part of a group of routers located on a slice communication path where the number of identified slice communication pairs does not satisfy a given number of pairs condition from being the router causing the performance of the functional element to deteriorate.

[0255] For example, suppose that the slice communication performed by gNB102a using network slice B satisfies the pair count condition. On the other hand, suppose that the slice communication performed by gNB102c using network slice C does not satisfy the pair count condition.

[0256] In this case, "12000", "12001", "12002", "12011", "12012", "12021", "12022", "20001", "20003", ... may be excluded from the router ID of the router causing the functional element performance degradation. In other words, in this case, "20002" would be estimated as the router ID of the router causing the functional element performance degradation.

[0257] This approach allows for a more accurate estimation of the router causing the silent outage.

[0258] Furthermore, the performance management unit 88 may generate comprehensive performance index data for each network slice in which a functional element is available, by aggregating the performance index data generated by the monitoring function unit 72, thereby indicating the performance of the functional element in slice communication using that network slice. The policy manager unit 90 may then calculate the correlation decrease or correlation increase based on the comprehensive performance index data generated by the performance management unit 88.

[0259] In the above example, for each pair of slice communications performed by a functional element included in RAN32 (gNB102 in the above example), it is determined whether the correlation decrease rate satisfies the decrease criterion. Similarly, for each pair of slice communications performed by a functional element included in RAN32 (gNB102 in the above example), it is determined whether the correlation increase rate satisfies the increase criterion. In this case, the router causing the performance degradation of the functional element may be estimated from among the group of routers located on the path between RAN32 and the core network system 34 (the path between gNB102 and UPF50 in the above example).

[0260] In this embodiment, for each pair of slice communications performed by a functional element (e.g., UPF50) included in the core network system 34, it may be determined whether the correlation decrease satisfies the increase criterion. Alternatively, for each pair of slice communications performed by a functional element (e.g., UPF50) included in the core network system 34, it may be determined whether the correlation increase satisfies the increase criterion. Furthermore, the router causing the performance degradation of the functional element may be estimated from among the group of routers present on the path between RAN32 and the core network system 34. In this case, for example, it may be determined whether the correlation decrease satisfies the decrease criterion based on a performance index value related to UPF50. Alternatively, it may be determined whether the correlation increase satisfies the increase criterion based on a performance index value related to UPF50.

[0261] Furthermore, the present invention can also be applied to estimating routers that are the cause of silent failures in network slices on paths other than the path between RAN32 and the core network system 34.

[0262] For example, the router causing the silent failure in the network slice in the path (midhall) between CU44 and DU42 may be estimated.

[0263] In this case, the policy manager unit 90 may determine whether the correlation reduction rate satisfies the reduction criteria for each pair of slice communications performed by CU44. Alternatively, the policy manager unit 90 may determine whether the correlation increase rate satisfies the increase criteria for each pair of slice communications performed by CU44. Furthermore, the router causing the performance degradation of CU44 may be estimated from among the group of routers present on the path between CU44 and DU42.

[0264] Furthermore, the performance index data values ​​may represent the performance of functional elements in the user plane, or they may represent the performance of functional elements in the control plane.

[0265] Furthermore, if the degree of correlation decrease for a slice communication pair in the user plane is determined to satisfy the decrease criterion, or if the degree of correlation increase for a slice communication pair in the user plane is determined to satisfy the increase criterion, the router that is causing the performance of the functional element to deteriorate among the router group which is a component of the user plane may be estimated.

[0266] Furthermore, if the degree of correlation decrease for a slice communication pair in the control plane is determined to satisfy the decrease criterion, or if the degree of correlation increase for a slice communication pair in the control plane is determined to satisfy the increase criterion, the router causing the performance degradation of a functional element may be estimated from among the router group that are components of the control plane.

[0267] Furthermore, even if the degree of correlation decrease for a slice communication pair in the control plane is determined to satisfy the decrease judgment condition, or if the degree of correlation increase is determined to satisfy the increase judgment condition, the router causing the performance degradation of the functional element may be estimated from among the router group which is a component of the control plane and the user plane.

[0268] Furthermore, the router causing the performance degradation of a functional element may be estimated from among the routers that are components of one or more network slice subnet instances, which are located along the path of the slice communication. For example, the router causing the performance degradation of a functional element may be estimated from among the routers in the backhaul portion located along the path. Alternatively, for example, the router causing the performance degradation of a functional element may be estimated from among the routers in the midhaul portion located along the path.

[0269] Here, an example of the process flow for estimating the router causing the performance degradation of a functional element, as performed in the platform system 30 according to this embodiment, will be explained with reference to the flowchart illustrated in Figure 15.

[0270] In the following example, it is assumed that the correlation data and correlation reduction data described above have already been generated for all slice communication pairs.

[0271] First, the policy manager unit 90 determines whether the correlation reduction degree associated with each slice communication pair satisfies the reduction determination condition (S101).

[0272] Then, the policy manager unit 90 identifies the number of slice communication pairs that contain the slice communication in question and whose correlation reduction degree associated with the pair satisfies the reduction determination condition (number of relevant pairs) for all slice communications (S102).

[0273] Then, the policy manager unit 90 identifies slice communications that satisfy the pair count condition based on the number of relevant pairs identified in the process shown in S102 (S103).

[0274] Then, the policy manager unit 90 identifies the group of routers located on the path of the slice communication identified in the process shown in S103 (S104).

[0275] Then, the policy manager unit 90 estimates the routers included in the group of routers identified in the process shown in S104 as the routers causing the performance degradation of the functional elements related to the slice communication (S105), and the process shown in this example is terminated.

[0276] However, the present invention is not limited to the embodiments described above.

[0277] For example, the functional unit according to this embodiment is not limited to that shown in Figure 3.

[0278] Furthermore, the functional unit according to this embodiment does not need to be an NF in 5G. For example, the functional unit according to this embodiment may be a network node in 4G, such as an eNodeB, vDU, vCU, P-GW (Packet Data Network Gateway), S-GW (Serving Gateway), MME (Mobility Management Entity), or HSS (Home Subscriber Server).

[0279] Furthermore, the division of roles for each function shown in Figure 5 is not limited to those described above.

[0280] Further, the functional unit according to the present embodiment may be implemented using a hypervisor type or host type virtualization technology instead of a container type virtualization technology. Also, the functional unit according to the present embodiment does not necessarily have to be implemented by software, and may be implemented by hardware such as an electronic circuit. Further, the functional unit according to the present embodiment may be implemented by a combination of an electronic circuit and software.

[0281] The technology described in the present disclosure can also be expressed as follows. [1] For each of a plurality of network slices constructed in a communication system, router group data storage means for storing router group data indicating a router group constituting the network slice; a correlation reduction degree calculation means for calculating a correlation reduction degree which is a degree of decrease in the strength of correlation between a performance index value indicating the performance of a functional element performing one slice communication which is a slice communication performed by any functional element included in the communication system using any network slice, and a performance index value indicating the performance of a functional element performing the other slice communication which is a slice communication different from the one slice communication and performed by any functional element included in the communication system using any network slice, and which is associated with a pair of the one slice communication and the other slice communication; a decrease determination means for determining, for each of a plurality of the pairs, whether or not the correlation reduction degree associated with the pair satisfies a given decrease determination condition; a pair number specifying means for specifying, for each of a plurality of slice communications performed in the communication system, the number of pairs that include the slice communication and for which the correlation reduction degree associated with the pair satisfies the decrease determination condition; At least one router included in a router group that exists on a path of the slice communication, which is specified based on the router group data and for which the number of specified pairs satisfies a given pair number condition, is estimated as a router that is the cause of the degradation in the performance of the functional element related to the slice communication, by router estimation means. A router estimation system including the same. [2] The router estimation means estimates at least one router included in a router group that exists on a path of the slice communication for which the number of specified pairs is greater than a predetermined number, as a router that is the cause of the degradation in the performance of the functional element related to the slice communication. The router estimation system according to [1]. [3] For each of a plurality of slice communications performed in the communication system, ratio specifying means for specifying a ratio of the number of pairs included in the slice communication for which a correlation decrease degree associated with the pair satisfies the decrease determination condition, to the total number of pairs included in the slice communication. The router estimation means estimates at least one router included in a router group that exists on a path of the slice communication for which the specified ratio is greater than a predetermined ratio, as a router that is the cause of the degradation in the performance of the functional element related to the slice communication. The router estimation system according to [1]. [4] The communication system further includes classification means for classifying a plurality of slice communications performed in the communication system into a plurality of slice communication groups based on the strength of the correlation specified for each pair of the slice communications. The correlation reduction calculation means calculates, for each of the plurality of slice communication groups, the degree of correlation reduction, which is the degree of decrease in the strength of the correlation between a performance index value indicating the performance of the functional element that performs the first slice communication in the first slice communication and a performance index value indicating the performance of the functional element that performs the second slice communication in the other slice communication, associated with a pair of slice communications: one slice communication that is included in the slice communication group and another slice communication that is included in the slice communication group but is different from the first slice communication. The reduction determination means determines, for each of the plurality of slice communication groups, whether the degree of correlation reduction associated with each pair of slice communications included in the slice communication group satisfies the reduction determination condition corresponding to the slice communication group. [1] The router estimation system described above. [5] The system further includes a threshold determination means for each of the plurality of slice communication groups, which determines a threshold corresponding to the slice communication group based on the total number of slice communications included in the slice communication group, The router estimation means estimates at least one router that is included in the group of routers located on the path of the slice communication, where the value representing the number of identified pairs is greater than the threshold corresponding to the slice communication group containing the pair, as the router causing the performance of the functional element related to the slice communication to be degraded. [4] The router estimation system described. [6] The method further includes, for each slice communication included in the slice communication group, a ratio identification means for identifying the ratio of the number of pairs containing the slice communication in which the correlation reduction degree associated with the pair satisfies the reduction determination condition, relative to the total number of pairs containing the slice communication. The router estimation means estimates at least one router included in the group of routers located on the path of the slice communication, where the identified proportion is greater than a predetermined proportion, as the router causing the performance of the functional element related to the slice communication to be degraded. [4] The router estimation system described. [7] The router estimation means estimates, when there are multiple slice communications where the number of identified pairs satisfies the pair count condition, that at least one router included in any of the router groups present on the path of each of the multiple slice communications is the router causing the performance degradation of the multiple functional elements related to the multiple slice communications. A router estimation system as described in any one of items [1] through [6]. [8] Correlation increase calculation means for calculating the correlation increase degree, which is the degree of increase in the strength of the correlation between a pair of slice communications, one of which is a slice communication performed by any functional element included in the communication system using any network slice, and another slice communication, one of which is a slice communication performed by any functional element included in the communication system using any network slice but different from the first slice communication, and a performance index value indicating the performance of the functional element performing the first slice communication in the first slice communication, associated with the first slice communication; The system further includes an increase determination means for determining whether the correlation increase associated with each of the plurality of pairs satisfies a given increase determination condition, The router estimation means estimates, when the correlation increase associated with the pair satisfies the increase determination condition, that at least one router included in either the first router group located on the path of one slice communication included in the pair, and the second router group located on the path of the other slice communication included in the pair, which are identified based on the router group data, as the router causing the performance of the functional element related to the pair to deteriorate. A router estimation system as described in any one of items [1] through [7]. [9] The correlation increase is the increase in the correlation coefficient of the performance index value. [8] The router estimation system described above.

[10] The router estimation means excludes from the cause router at least one router that is included in the group of routers located on the path of the slice communication if the number of identified pairs does not satisfy the pair count condition. The router estimation systems described in [1] through [9].

[11] The network slice in which the first slice communication takes place is the same network slice in which the second slice communication takes place. A router estimation system as described in any one of items [1] through

[10] .

[12] The network slice in which the first slice communication takes place is a different network slice from the network slice in which the second slice communication takes place. A router estimation system as described in any one of items [1] through

[10] .

[13] The aforementioned functional element is a functional element included in the wireless access network of the communication system, The router estimation means estimates the router causing the problem from among the group of routers located on the path between the wireless access network and the core network system of the communication system. A router estimation system as described in any one of the items [1] through

[12] .

[14] The aforementioned functional element is a functional element included in the core network system of the communication system, The router estimation means estimates the router causing the problem from among the group of routers present on the path between the core network system and the wireless access network of the communication system. A router estimation system as described in any one of the items [1] through

[12] .

[15] The aforementioned functional element is a CU (Central Unit), The router estimation means estimates the router causing the problem from among the group of routers present on the path between the CU and the DU (Distributed Unit) included in the communication system. A router estimation system as described in any one of the items [1] through

[12] .

[16] The aforementioned correlation reduction is the reduction in the correlation coefficient of the performance index value. A router estimation system as described in any one of items [1] through

[15] .

[17] The aforementioned path is a path through which packets are forwarded by segment routing. A router estimation system as described in any one of the items [1] through

[16] .

[18] The aforementioned functional element is a network service or network function. A router estimation system as described in any one of items [1] through

[17] .

[19] For each of the multiple network slices built into the communication system, router group data indicating the group of routers that constitute that network slice is stored. To calculate the correlation reduction degree, which is the degree of decrease in the strength of the correlation between a pair of slice communications, one of which is a slice communication performed by any functional element included in the communication system using any network slice, and another slice communication, one of which is a slice communication performed by any functional element included in the communication system using any network slice but different from the first slice communication, and a performance index value indicating the performance of the functional element performing the first slice communication in the other slice communication, associated with the first slice communication. For each of the multiple aforementioned pairs, it is determined whether the degree of correlation reduction associated with that pair satisfies a given reduction determination condition, For each of a plurality of slice communications performed in the communication system, specifying the number of pairs included in the slice communication, among which the degree of correlation decrease associated with the pair satisfies the decrease determination condition; Based on the router group data, estimating at least one router included in the router group existing on the path of the slice communication for which the number of specified pairs satisfies a given pair number condition, as the router causing the performance degradation of the functional element related to the slice communication; A router estimation method including the above.

Claims

1. Router group data storage processing for storing router group data indicating the group of routers constituting each of a plurality of network slices constructed in a communication system, A correlation reduction calculation process calculates a correlation reduction degree, which is the degree of decrease in the strength of the correlation between a pair of slice communications: one slice communication in which any functional element included in the communication system performs slice communication using any network slice, and the other slice communication in which any functional element included in the communication system performs communication using any network slice, but which is a different slice communication from the first slice communication, and a performance index value indicating the performance of the functional element performing the first slice communication in the other slice communication. A reduction determination process for each of the multiple aforementioned pairs, which determines whether the degree of correlation reduction associated with the pair satisfies a given reduction determination condition, For each of the multiple slice communications performed in the communication system, a pair number identification process is performed to identify the number of pairs that include the slice communication and whose correlation reduction degree associated with the pair satisfies the reduction determination condition. A router estimation process that estimates at least one router included in the router group located on the path of the slice communication, which is identified based on the router group data and whose number of identified pairs satisfies a given number of pairs condition, as the router causing the performance of the functional element related to the slice communication to be degraded; A router estimation system that performs this task.

2. In the router estimation process, at least one router included in the group of routers located along the slice communication path where the number of identified pairs exceeds a predetermined number is estimated to be the router causing the performance degradation of the functional element related to the slice communication. The router estimation system according to claim 1.

3. For each of the multiple slice communications performed in the communication system, a ratio identification process is performed to identify the ratio of the number of pairs containing the slice communication in which the correlation reduction degree associated with the pair satisfies the reduction determination condition, relative to the total number of pairs containing the slice communication. In the router estimation process, at least one router included in the group of routers located on the path of the slice communication, where the identified proportion is greater than a predetermined proportion, is estimated to be the router causing the performance degradation of the functional element related to the slice communication. The router estimation system according to claim 1.

4. A classification process is performed which classifies a plurality of slice communications performed in the communication system into a plurality of slice communication groups based on the strength of the correlation identified for each pair of slice communications, In the correlation reduction calculation process described above, for each of the multiple slice communication groups, a correlation reduction is calculated, which is the degree of decrease in the strength of the correlation between a performance index value indicating the performance of the functional element that performs the first slice communication in the first slice communication and a performance index value indicating the performance of the functional element that performs the second slice communication in the other slice communication, associated with a pair of slice communications: one slice communication that is included in the slice communication group and another slice communication that is included in the slice communication group but is different from the first slice communication. In the reduction determination process, for each of the plurality of slice communication groups, it is determined whether the degree of correlation reduction associated with each pair of slice communications included in the slice communication group satisfies the reduction determination condition corresponding to the slice communication group. The router estimation system according to claim 1.

5. A threshold determination process is performed for each of the plurality of slice communication groups, which determines a threshold corresponding to the slice communication group based on the total number of slice communications included in the slice communication group. In the router estimation process, at least one router included in the group of routers located on the path of the slice communication, where the value representing the number of identified pairs is greater than the threshold corresponding to the slice communication group containing the pair, is estimated to be the router causing the performance degradation of the functional element related to the slice communication. The router estimation system according to claim 4.

6. For each slice communication included in the slice communication group, a ratio identification process is performed to identify the ratio of the number of pairs containing the slice communication in which the correlation reduction degree associated with the pair satisfies the reduction determination condition, relative to the total number of pairs containing the slice communication. In the router estimation process, at least one router included in the group of routers located on the path of the slice communication, where the identified proportion is greater than a predetermined proportion, is estimated to be the router causing the performance degradation of the functional element related to the slice communication. The router estimation system according to claim 4.

7. In the router estimation process, if the number of identified pairs is large enough to satisfy the pair count condition for multiple slice communications, at least one router included in any of the router groups located on the paths of each of those multiple slice communications is estimated to be the router causing the performance degradation of the multiple functional elements related to those multiple slice communications. The router estimation system according to claim 1.

8. A correlation increase calculation process that calculates a correlation increase, which is the degree of increase in the strength of the correlation between a pair of slice communications, one of which is a slice communication performed by any functional element included in the communication system using any network slice, and another slice communication, one of which is a slice communication performed by any functional element included in the communication system using any network slice and is different from the first slice communication, and a performance index value indicating the performance of the functional element performing the other slice communication in the other slice communication. For each of the multiple pairs, an increase determination process is performed to determine whether the correlation increase associated with the pair satisfies a given increase determination condition. In the router estimation process, if the correlation increase associated with the pair satisfies the increase determination condition, at least one router included in either the first router group located on the path of one slice communication included in the pair, and the second router group located on the path of the other slice communication included in the pair, which are identified based on the router group data, is estimated to be the router causing the performance degradation of the functional element related to the pair. The router estimation system according to claim 1.

9. The correlation increase is the increase in the correlation coefficient of the performance index value. The router estimation system according to claim 8.

10. In the router estimation process, at least one router that is part of the group of routers located on the path of the slice communication and whose number of identified pairs does not satisfy the pair count condition is excluded from the routers causing the problem. The router estimation system according to claim 1.

11. The network slice in which the first slice communication takes place is the same network slice in which the second slice communication takes place. The router estimation system according to claim 1.

12. The network slice in which the first slice communication takes place is a different network slice from the network slice in which the second slice communication takes place. The router estimation system according to claim 1.

13. The aforementioned functional element is a functional element included in the wireless access network of the communication system, In the router estimation process, the router causing the problem is estimated from among the group of routers located on the path between the wireless access network and the core network system of the communication system. The router estimation system according to claim 1.

14. The aforementioned functional element is a functional element included in the core network system of the communication system, In the router estimation process, the router causing the problem is estimated from among the group of routers located on the path between the core network system and the wireless access network of the communication system. The router estimation system according to claim 1.

15. The aforementioned functional element is a CU (Central Unit), In the router estimation process, the router causing the problem is estimated from among the group of routers located on the path between the CU and the DU (Distributed Unit) included in the communication system. The router estimation system according to claim 1.

16. The aforementioned correlation reduction is the reduction in the correlation coefficient of the performance index value. The router estimation system according to claim 1.

17. The aforementioned path is a path through which packets are forwarded by segment routing. The router estimation system according to claim 1.

18. The aforementioned functional element is a network service or network function. The router estimation system according to claim 1.

19. For each of the multiple network slices built into the communication system, router group data indicating the group of routers that constitute that network slice is stored. To calculate the correlation reduction degree, which is the degree of decrease in the strength of the correlation between a pair of slice communications, one of which is a slice communication performed by any functional element included in the communication system using any network slice, and another slice communication, one of which is a slice communication performed by any functional element included in the communication system using any network slice but different from the first slice communication, and a performance index value indicating the performance of the functional element performing the first slice communication in the other slice communication, associated with the first slice communication. For each of the multiple aforementioned pairs, it is determined whether the degree of correlation reduction associated with that pair satisfies a given reduction determination condition, For each of the multiple slice communications performed in the aforementioned communication system, the number of pairs containing the slice communications in which the correlation reduction degree associated with the pair satisfies the reduction determination condition is identified, Based on the router group data, at least one router included in the router group located on the path of the slice communication, whose number of identified pairs satisfies a given number of pairs condition, is estimated to be the router causing the performance of the functional element related to the slice communication to be degraded. Router estimation method including

Images